Cardiac resident nestin<sup>+</sup> cells participate in reparative vascularisation
Bibliographic record
Abstract
The rodent heart contains a population of nestin((+)) cells derived from the embryonic neural crest and migrate to the scar after myocardial infarction (MI). The present study tested the hypothesis that intron 2 of the nestin gene drives expression and a subpopulation of nestin((+)) cells participate in reparative vascularisation. The directed expression of the green fluorescent protein (GFP) by the second intron of the nestin gene identified GFP/nestin((+)) cells intercalated among ventricular myocytes in the heart of normal transgenic mice. Ischemic injury led to the migration of GFP((+)) cells to the scar and a subpopulation was detected in CD31/nestin((+)) endothelial cells of newly formed blood vessels. The direct contribution to reparative vascularisation provided the impetus to test the hypothesis that increasing the population of nestin((+)) cells in the infarcted heart will improve scar healing. Skin-derived cells isolated from E18 Sprague-Dawley rats grew as spheres, expressed nestin, sox2, neural crest-related transcriptional genes and a panel of peptide growth factors. Skin-derived cells transplanted in the non-infarcted left ventricle of 3-day post-MI rats migrated to the peri-infarct/infarct region and remained engrafted for 21 days. A significantly smaller infarct, increased number of small calibre blood vessels and improved ventricular function were observed in engrafted infarcted rat hearts. Thus, the second intron of the nestin gene drives expression in the mouse heart and a subpopulation of GFP/nestin((+)) cells directly participate in reparative vascularisation. Increasing the population of nestin((+)) cells via the transplantation of skin-derived cells represents a potential approach to limit ischemic damage to the heart.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".